Satellitelab
Bibliographic record
Abstract
Planetary-scale network testbeds like PlanetLab and RON have become indispensable for evaluating prototypes of distributed systems under realistic Internet conditions. However, current testbeds lack the heterogeneity that characterizes the commercial Internet. For example, most testbed nodes are connected to well-provisioned research networks, whereas most Internet nodes are in edge networks. In this paper, we present the design, implementation, and evaluation of SatelliteLab, a testbed that includes nodes from a diverse set of Internet edge networks. SatelliteLab has a two-tier architecture, in which well-provisioned nodes called planets form the core, and lightweight nodes called satellites connect to the planets from the periphery. The application code of an experiment runs on the planets, whereas the satellites only forward network traffic. Thus, the traffic is subjected to the network conditions of the satellites, which greatly improves the testbed's network heterogeneity. The separation of code execution and traffic forwarding enables satellites to remain lightweight, which lowers the barrier to entry for Internet edge nodes. Our prototype of SatelliteLab uses PlanetLab nodes as planets and a set of 32 volunteered satellites with diverse network characteristics. These satellites consist of desktops, laptops, and handhelds connected to the Internet via cable, DSL, ISDN, Wi-Fi, Bluetooth, and cellular links. We evaluate SatelliteLab's design, and we demonstrate the benefits of evaluating applications on SatelliteLab.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.160 | 0.083 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".